For as long as retailers have been selling products and services, they have been seeking ways to increase profits. Accordingly, many retailers create new initiatives that they believe will have a positive impact on sales. These initiatives usually cover various aspects of business operations that drive profits. For example, retailers may change product prices, move products to different locations of a sales floor, change the amount of space allocated to each product, test new products, add or reduce sales staff, introduce new concepts (e.g., in-store kiosks), remodel older stores, and test new marketing campaigns. Retailers may test these new initiatives in selected test locations (i.e., certain stores) and subsequently determine whether to introduce the initiatives to remaining business locations based on the success of the initiatives at the selected test locations. Historically, retailer management have used business instinct and/or anecdotal evidence to assess the success of the initiatives in order to make a decision whether to implement the initiatives at the rest of its business locations.
In recent years, however, some retailers have become more structured and analytical in their set up and analysis of tests. These retailers collect performance metrics, such as sales and gross profit data from the test locations and analyze the data using conventional software products, such as spreadsheet or statistical software packages. Retailers may measure the change in the performance metrics at the locations that introduced the new initiatives relative to a change in the same metrics at a control group of locations that did not implement the initiatives. In doing so, the retailers attempt to identify the impact of the initiatives on the performance metrics in order to determine whether they provide a positive return on investment. Accordingly, these retailers can make an informed decision whether to extend the concept to remaining locations in a business network.
Even the most advance retailers, however, are only able to assess the average impact of the tested initiative. In other words, a retailer that tests a new store layout in 20 stores, can only interpret the average sales impact of the new layout in those 20 stores. Further, they may lack the ability to understand how that average impact varied among different types of stores and which of the other non-tested stores in their business network would benefit from implementation of the new layout. For example, the retailer will not be able to determine whether, based on the 20-store test, the new layout works better in certain demographic or competitive environments, in certain locations (e.g., shopping centers), or in larger or smaller stores. Because of these shortcomings, a retailer may not be able to make an informed business decision regarding which non-tested stores should implement the new layout. Instead, the retailer must make a global decision that affects the entire business network. That is, either roll out the tested initiative to all stores in the network or eliminate the initiative altogether.
Often, an initiative will work well in some location types and not in others. It would therefore be preferable if the retailer could only extend the initiative to those stores most likely to benefit from the initiative instead of extending the imitative to either all stores or none at all.